def __init__(self, time_series, api=None):
self.resource_id = None
self.input_fields = []
self.objective_fields = []
self.all_numeric_objectives = False
self.period = 1
self.ets_models = {}
self.error = None
self.damped_trend = None
self.seasonality = None
self.trend = None
self.time_range = {}
self.field_parameters = {}
self._forecast = {}
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(time_series, dict) and \
not check_model_fields(time_series):
# if the fields used by the logistic regression are not
# available, use only ID to retrieve it again
time_series = get_time_series_id( \
time_series)
self.resource_id = time_series
if not (isinstance(time_series, dict)
and 'resource' in time_series and
time_series['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_time_series_id(time_series)
if self.resource_id is None:
raise Exception(
api.error_message(time_series,
resource_type='time_series',
method='get'))
query_string = ONLY_MODEL
time_series = retrieve_resource(
api, self.resource_id, query_string=query_string)
else:
self.resource_id = get_time_series_id(time_series)
if 'object' in time_series and \
isinstance(time_series['object'], dict):
time_series = time_series['object']
try:
self.input_fields = time_series.get("input_fields", [])
self._forecast = time_series.get("forecast")
self.objective_fields = time_series.get(
"objective_fields", [])
objective_field = time_series['objective_field'] if \
time_series.get('objective_field') else \
time_series['objective_fields']
except KeyError:
raise ValueError("Failed to find the time series expected "
"JSON structure. Check your arguments.")
if 'time_series' in time_series and \
isinstance(time_series['time_series'], dict):
status = get_status(time_series)
if 'code' in status and status['code'] == FINISHED:
time_series_info = time_series['time_series']
fields = time_series_info.get('fields', {})
self.fields = fields
if not self.input_fields:
self.input_fields = [ \
field_id for field_id, _ in
sorted(self.fields.items(),
key=lambda x: x[1].get("column_number"))]
self.all_numeric_objectives = time_series_info.get( \
'all_numeric_objectives')
self.period = time_series_info.get('period', 1)
self.ets_models = time_series_info.get('ets_models', {})
self.error = time_series_info.get('error')
self.damped_trend = time_series_info.get('damped_trend')
self.seasonality = time_series_info.get('seasonality')
self.trend = time_series_info.get('trend')
self.time_range = time_series_info.get('time_range')
self.field_parameters = time_series_info.get( \
'field_parameters', {})
objective_id = extract_objective(objective_field)
ModelFields.__init__(
self, fields,
objective_id=objective_id)
else:
raise Exception("The time series isn't finished yet")
else:
raise Exception("Cannot create the TimeSeries instance."
" Could not find the 'time_series' key"
" in the resource:\n\n%s" %
time_series)
def __init__(self, time_series, api=None):
self.resource_id = None
self.input_fields = []
self.objective_fields = []
self.all_numeric_objectives = False
self.period = 1
self.ets_models = {}
self.error = None
self.damped_trend = None
self.seasonality = None
self.trend = None
self.time_range = {}
self.field_parameters = {}
self._forecast = []
# checks whether the information needed for local predictions is in
# the first argument
if isinstance(time_series, dict) and \
not check_model_fields(time_series):
# if the fields used by the logistic regression are not
# available, use only ID to retrieve it again
time_series = get_time_series_id( \
time_series)
self.resource_id = time_series
if not (isinstance(time_series, dict)
and 'resource' in time_series and
time_series['resource'] is not None):
if api is None:
api = BigML(storage=STORAGE)
self.resource_id = get_time_series_id(time_series)
if self.resource_id is None:
raise Exception(
api.error_message(time_series,
resource_type='time_series',
method='get'))
query_string = ONLY_MODEL
time_series = retrieve_resource(
api, self.resource_id, query_string=query_string)
else:
self.resource_id = get_time_series_id(time_series)
if 'object' in time_series and \
isinstance(time_series['object'], dict):
time_series = time_series['object']
try:
self.input_fields = time_series.get("input_fields", [])
self._forecast = time_series.get("forecast")
self.objective_fields = time_series.get(
"objective_fields", [])
objective_field = time_series['objective_field'] if \
time_series.get('objective_field') else \
time_series['objective_fields']
except KeyError:
raise ValueError("Failed to find the time series expected "
"JSON structure. Check your arguments.")
if 'time_series' in time_series and \
isinstance(time_series['time_series'], dict):
status = get_status(time_series)
if 'code' in status and status['code'] == FINISHED:
time_series_info = time_series['time_series']
fields = time_series_info.get('fields', {})
self.fields = fields
if not self.input_fields:
self.input_fields = [ \
field_id for field_id, _ in
sorted(self.fields.items(),
key=lambda x: x[1].get("column_number"))]
self.all_numeric_objectives = time_series_info.get( \
'all_numeric_objectives')
self.period = time_series_info.get('period', 1)
self.ets_models = time_series_info.get('ets_models', {})
self.error = time_series_info.get('error')
self.damped_trend = time_series_info.get('damped_trend')
self.seasonality = time_series_info.get('seasonality')
self.trend = time_series_info.get('trend')
self.time_range = time_series_info.get('time_range')
self.field_parameters = time_series_info.get( \
'field_parameters', {})
objective_id = extract_objective(objective_field)
ModelFields.__init__(
self, fields,
objective_id=objective_id)
else:
raise Exception("The time series isn't finished yet")
else:
raise Exception("Cannot create the TimeSeries instance."
" Could not find the 'time_series' key"
" in the resource:\n\n%s" %
time_series)
def __init__(self, time_series, api=None, cache_get=None):
if use_cache(cache_get):
# using a cache to store the model attributes
self.__dict__ = load(get_time_series_id(time_series), cache_get)
return
self.resource_id = None
self.input_fields = []
self.default_numeric_value = None
self.objective_fields = []
self.all_numeric_objectives = False
self.period = 1
self.ets_models = {}
self.error = None
self.damped_trend = None
self.seasonality = None
self.trend = None
self.time_range = {}
self.field_parameters = {}
self._forecast = {}
api = get_api_connection(api)
self.resource_id, time_series = get_resource_dict( \
time_series, "timeseries", api=api)
if 'object' in time_series and \
isinstance(time_series['object'], dict):
time_series = time_series['object']
try:
self.input_fields = time_series.get("input_fields", [])
self.default_numeric_value = time_series.get( \
"default_numeric_value")
self._forecast = time_series.get("forecast")
self.objective_fields = time_series.get("objective_fields", [])
objective_field = time_series['objective_field'] if \
time_series.get('objective_field') else \
time_series['objective_fields']
except KeyError:
raise ValueError("Failed to find the time series expected "
"JSON structure. Check your arguments.")
if 'time_series' in time_series and \
isinstance(time_series['time_series'], dict):
status = get_status(time_series)
if 'code' in status and status['code'] == FINISHED:
time_series_info = time_series['time_series']
fields = time_series_info.get('fields', {})
self.fields = fields
if not self.input_fields:
self.input_fields = [ \
field_id for field_id, _ in
sorted(list(self.fields.items()),
key=lambda x: x[1].get("column_number"))]
self.all_numeric_objectives = time_series_info.get( \
'all_numeric_objectives')
self.period = time_series_info.get('period', 1)
self.ets_models = time_series_info.get('ets_models', {})
self.error = time_series_info.get('error')
self.damped_trend = time_series_info.get('damped_trend')
self.seasonality = time_series_info.get('seasonality')
self.trend = time_series_info.get('trend')
self.time_range = time_series_info.get('time_range')
self.field_parameters = time_series_info.get( \
'field_parameters', {})
objective_id = extract_objective(objective_field)
ModelFields.__init__(self, fields, objective_id=objective_id)
else:
raise Exception("The time series isn't finished yet")
else:
raise Exception("Cannot create the TimeSeries instance."
" Could not find the 'time_series' key"
" in the resource:\n\n%s" % time_series)